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Decoupled phenotypic variation between floral and vegetative traits: distinguishing between developmental and environmental correlations
- Pélabon, Christophe, Osler, Nora C., Diekmann, Martin, Graae, Bente J.
- Annals of botany 2013 v.111 no.5 pp. 935-944
- Campanula rotundifolia, cold, covariance, flowers, leaves, phenotypic correlation, phenotypic variation, pollination, temperature
- Background and Aims In species with specialized pollination, floral traits are expected to be relatively invariant and decoupled from the phenotypic variation affecting vegetative traits. However, inferring the degree of decoupling between morphological characters from patterns of phenotypic correlations is difficult because phenotypic correlations result from the superimposition of several sources of covariance. In this study it is hypothesized that, in some cases, negative environmental correlations generated by non-congruent reaction norms across traits overshadow positive developmental correlations and generate a decoupling of the phenotypic variation between vegetative and floral traits. Methods To test this hypothesis, Campanula rotundifolia were grown from two distinct populations under two temperature treatments, and patterns of correlation were analysed between leaf size and flower size within and among treatments. Key Results Flower size was less sensitive to temperature variation than leaf size. Furthermore, flower size and leaf size showed temperature-induced reaction norms in opposite directions. Flower size decreased with an increasing temperature, while leaf size increased. Consequently, among treatments, correlations between leaf size and flower size were negative or absent, while, within treatments, these correlations were positive or absent in the cold and warm environments, respectively. Conclusions These results confirm that the decoupling of the phenotypic variation between vegetative and floral traits can be dependent on the environment. They also underline the importance of distinguishing sources of phenotypic covariance when testing hypotheses about phenotypic integration.